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HorcruxNo13
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vit-base-patch16-224

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +38 -38
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8566666666666667
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  - name: Precision
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  type: precision
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- value: 0.8522571872571872
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  - name: Recall
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  type: recall
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- value: 0.8566666666666667
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,11 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4410
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- - Accuracy: 0.8567
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- - Precision: 0.8523
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- - Recall: 0.8567
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- - F1 Score: 0.8517
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  ## Model description
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@@ -78,36 +78,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 0.5841 | 0.7333 | 0.6770 | 0.7333 | 0.6479 |
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- | No log | 2.0 | 8 | 0.5727 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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- | No log | 3.0 | 12 | 0.6089 | 0.7208 | 0.7222 | 0.7208 | 0.7215 |
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- | No log | 4.0 | 16 | 0.5332 | 0.7458 | 0.7205 | 0.7458 | 0.6727 |
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- | No log | 5.0 | 20 | 0.5314 | 0.7625 | 0.7410 | 0.7625 | 0.7416 |
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- | No log | 6.0 | 24 | 0.5284 | 0.7583 | 0.7486 | 0.7583 | 0.6959 |
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- | No log | 7.0 | 28 | 0.5220 | 0.775 | 0.7700 | 0.775 | 0.7286 |
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- | 0.5564 | 8.0 | 32 | 0.5204 | 0.7833 | 0.7740 | 0.7833 | 0.7481 |
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- | 0.5564 | 9.0 | 36 | 0.5044 | 0.7708 | 0.7616 | 0.7708 | 0.7650 |
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- | 0.5564 | 10.0 | 40 | 0.4845 | 0.8125 | 0.8051 | 0.8125 | 0.7941 |
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- | 0.5564 | 11.0 | 44 | 0.4921 | 0.7833 | 0.7726 | 0.7833 | 0.7757 |
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- | 0.5564 | 12.0 | 48 | 0.4792 | 0.8167 | 0.8098 | 0.8167 | 0.7996 |
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- | 0.5564 | 13.0 | 52 | 0.4825 | 0.8 | 0.7889 | 0.8 | 0.7901 |
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- | 0.5564 | 14.0 | 56 | 0.4987 | 0.8083 | 0.7989 | 0.8083 | 0.8002 |
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- | 0.3176 | 15.0 | 60 | 0.4970 | 0.8208 | 0.8144 | 0.8208 | 0.8050 |
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- | 0.3176 | 16.0 | 64 | 0.5076 | 0.8083 | 0.7983 | 0.8083 | 0.7923 |
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- | 0.3176 | 17.0 | 68 | 0.5227 | 0.8083 | 0.7979 | 0.8083 | 0.7941 |
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- | 0.3176 | 18.0 | 72 | 0.5132 | 0.8042 | 0.7928 | 0.8042 | 0.7905 |
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- | 0.3176 | 19.0 | 76 | 0.5081 | 0.8167 | 0.8087 | 0.8167 | 0.8014 |
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- | 0.3176 | 20.0 | 80 | 0.5140 | 0.8292 | 0.8220 | 0.8292 | 0.8187 |
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- | 0.3176 | 21.0 | 84 | 0.5392 | 0.8125 | 0.8032 | 0.8125 | 0.7977 |
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- | 0.3176 | 22.0 | 88 | 0.5175 | 0.7958 | 0.7829 | 0.7958 | 0.7815 |
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- | 0.1778 | 23.0 | 92 | 0.5109 | 0.8125 | 0.8032 | 0.8125 | 0.7977 |
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- | 0.1778 | 24.0 | 96 | 0.4961 | 0.8292 | 0.8217 | 0.8292 | 0.8213 |
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- | 0.1778 | 25.0 | 100 | 0.5251 | 0.8083 | 0.7979 | 0.8083 | 0.7941 |
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- | 0.1778 | 26.0 | 104 | 0.5192 | 0.8167 | 0.8075 | 0.8167 | 0.8046 |
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- | 0.1778 | 27.0 | 108 | 0.5030 | 0.8333 | 0.8274 | 0.8333 | 0.8286 |
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- | 0.1778 | 28.0 | 112 | 0.5031 | 0.8375 | 0.8310 | 0.8375 | 0.8300 |
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- | 0.1778 | 29.0 | 116 | 0.5164 | 0.8208 | 0.8127 | 0.8208 | 0.8083 |
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- | 0.1109 | 30.0 | 120 | 0.5192 | 0.8208 | 0.8127 | 0.8208 | 0.8083 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.78
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  - name: Precision
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  type: precision
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+ value: 0.781535758027584
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  - name: Recall
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  type: recall
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+ value: 0.78
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4819
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+ - Accuracy: 0.78
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+ - Precision: 0.7815
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+ - Recall: 0.78
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+ - F1 Score: 0.7807
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.5936 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
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+ | No log | 2.0 | 8 | 0.5702 | 0.7208 | 0.6468 | 0.7208 | 0.6283 |
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+ | No log | 3.0 | 12 | 0.5834 | 0.7125 | 0.6933 | 0.7125 | 0.7000 |
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+ | No log | 4.0 | 16 | 0.5471 | 0.7375 | 0.7034 | 0.7375 | 0.6846 |
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+ | No log | 5.0 | 20 | 0.5487 | 0.725 | 0.6938 | 0.725 | 0.6982 |
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+ | No log | 6.0 | 24 | 0.5253 | 0.7458 | 0.7182 | 0.7458 | 0.7116 |
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+ | No log | 7.0 | 28 | 0.5556 | 0.7417 | 0.7393 | 0.7417 | 0.7404 |
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+ | 0.5648 | 8.0 | 32 | 0.5183 | 0.7417 | 0.7155 | 0.7417 | 0.7165 |
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+ | 0.5648 | 9.0 | 36 | 0.5159 | 0.7667 | 0.7504 | 0.7667 | 0.7522 |
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+ | 0.5648 | 10.0 | 40 | 0.5137 | 0.7708 | 0.7579 | 0.7708 | 0.7609 |
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+ | 0.5648 | 11.0 | 44 | 0.5014 | 0.7833 | 0.7693 | 0.7833 | 0.7643 |
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+ | 0.5648 | 12.0 | 48 | 0.5157 | 0.75 | 0.7524 | 0.75 | 0.7511 |
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+ | 0.5648 | 13.0 | 52 | 0.5151 | 0.7417 | 0.7441 | 0.7417 | 0.7428 |
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+ | 0.5648 | 14.0 | 56 | 0.4908 | 0.7792 | 0.7653 | 0.7792 | 0.7663 |
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+ | 0.3814 | 15.0 | 60 | 0.4901 | 0.7833 | 0.7723 | 0.7833 | 0.7747 |
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+ | 0.3814 | 16.0 | 64 | 0.4993 | 0.7667 | 0.7689 | 0.7667 | 0.7677 |
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+ | 0.3814 | 17.0 | 68 | 0.4814 | 0.7792 | 0.7642 | 0.7792 | 0.7627 |
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+ | 0.3814 | 18.0 | 72 | 0.5165 | 0.7583 | 0.7796 | 0.7583 | 0.7656 |
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+ | 0.3814 | 19.0 | 76 | 0.4817 | 0.7958 | 0.7915 | 0.7958 | 0.7933 |
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+ | 0.3814 | 20.0 | 80 | 0.4748 | 0.8083 | 0.8036 | 0.8083 | 0.8054 |
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+ | 0.3814 | 21.0 | 84 | 0.4831 | 0.8042 | 0.8033 | 0.8042 | 0.8037 |
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+ | 0.3814 | 22.0 | 88 | 0.4795 | 0.8083 | 0.8013 | 0.8083 | 0.8032 |
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+ | 0.2354 | 23.0 | 92 | 0.5048 | 0.7708 | 0.7790 | 0.7708 | 0.7743 |
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+ | 0.2354 | 24.0 | 96 | 0.4838 | 0.8042 | 0.7974 | 0.8042 | 0.7995 |
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+ | 0.2354 | 25.0 | 100 | 0.4894 | 0.7833 | 0.7833 | 0.7833 | 0.7833 |
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+ | 0.2354 | 26.0 | 104 | 0.4852 | 0.8 | 0.7914 | 0.8 | 0.7933 |
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+ | 0.2354 | 27.0 | 108 | 0.4882 | 0.8 | 0.7982 | 0.8 | 0.7990 |
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+ | 0.2354 | 28.0 | 112 | 0.4932 | 0.7875 | 0.7929 | 0.7875 | 0.7898 |
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+ | 0.2354 | 29.0 | 116 | 0.4883 | 0.8083 | 0.8036 | 0.8083 | 0.8054 |
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+ | 0.1479 | 30.0 | 120 | 0.4886 | 0.8042 | 0.7974 | 0.8042 | 0.7995 |
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  ### Framework versions
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